Applied ML Engineer

Jobgether SRL; France; Sur place; EUR 85 000 - 120 000; Plein temps

Avantages offerts par ce poste

Location: France

Healthcare

Flexible work arrangements

Résumé du poste

Jobgether SRL is seeking an Applied ML Engineer based in France to bridge ML research, experimentation, and production engineering. You will convert ideas from research into rigorous experiments, building robust evaluation pipelines and production-grade tooling for repeatable experiments.

The role covers model evaluation, internals, inference infrastructure, backend systems, and user-facing experiences, with a focus on open-weight models, LLM inference, and verifiable results.

Qualifications

• Strong Python engineering skills with hands-on PyTorch experience.
• Experience with ML evaluation, datasets, baselines, metrics, calibration, false positives/negatives, and reproducibility.
• Professional software engineering experience beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, deployment, and documentation.
• Familiarity with open-weight models and practical knowledge of LLM inference systems.
• Ability to read ML research papers and implement methods from first principles.

Responsabilités

• Reproduce and evaluate machine learning research methods using open-weight and API-accessible models.
• Design evaluation datasets, probes, scoring approaches, baselines, calibration tests, and experiment harnesses.
• Work directly with model weights, logits, hidden states, activations, model APIs, and inference infrastructure when required.
• Build and extend evaluation infrastructure covering experiment runners, judges, persistence, orchestration, reporting, and reproducibility.
• Turn research workflows into intuitive product experiences, including experiment configuration, execution, traces, comparisons, reports, and review workflows.
• Investigate how verification methods behave when models are modified through fine-tuning, merging, quantization, distillation, safety removal, or deliberate evasion.
• Design controlled experiments that distinguish meaningful signals from artifacts, confounders, and misleading correlations.
• Produce clear technical reports that separate measured evidence from interpretation and hypotheses.
• Deliver production-quality systems with APIs, asynchronous jobs, databases, observability, testing, deployment, and documentation.
• Contribute across research, experimentation, engineering, and product as priorities evolve.
• During the first six months, reproduce and document at least one published model-provenance or verification method, including its capabilities, assumptions, and limitations.
• Build a repeatable model-verification runner with versioned inputs, artifacts, metrics, and reports, and make at least one verification workflow accessible through the product interface.
• Run controlled experiments across base, fine-tuned, merged, quantized, and known distilled models, improving understanding of when verification methods succeed, fail, and why.

Connaissances

Python

PyTorch

Transformers

React

TypeScript

Outils

PostgreSQL/pgvector

DSPy

LiteLLM

Temporal

Ray

vLLM

Description du poste

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied ML Engineer based in France.

As an Applied ML Engineer, you’ll work at the intersection of machine learning research, experimentation, and production engineering.

You’ll turn ideas from research papers into rigorous experiments, measurable evidence, and reliable products.

The role spans model evaluation, model internals, inference infrastructure, backend systems, and user-facing product experiences.

You’ll work hands-on with modern ML models, designing evaluations that reveal what methods can—and cannot—demonstrate.

You’ll also build production-grade tooling that makes complex experiments repeatable, observable, and accessible to users.

The environment values technical judgment, ownership, scientific rigor, and the ability to move comfortably across the technology stack.

It’s an opportunity to help transform emerging ML techniques into practical systems that people can trust.

Accountabilities

• Reproduce and evaluate machine learning research methods using open-weight and API-accessible models.
• Design evaluation datasets, probes, scoring approaches, baselines, calibration tests, and experiment harnesses.
• Work directly with model weights, logits, hidden states, activations, model APIs, and inference infrastructure when required.
• Build and extend evaluation infrastructure covering experiment runners, judges, persistence, orchestration, reporting, and reproducibility.
• Turn research workflows into intuitive product experiences, including experiment configuration, execution, traces, comparisons, reports, and review workflows.
• Investigate how verification methods behave when models are modified through fine-tuning, merging, quantization, distillation, safety removal, or deliberate evasion.
• Design controlled experiments that distinguish meaningful signals from artifacts, confounders, and misleading correlations.
• Produce clear technical reports that separate measured evidence from interpretation and hypotheses.
• Deliver production-quality systems with APIs, asynchronous jobs, databases, observability, testing, deployment, and documentation.
• Contribute across research, experimentation, engineering, and product as priorities evolve.
• During the first six months, reproduce and document at least one published model-provenance or verification method, including its capabilities, assumptions, and limitations.
• Build a repeatable model-verification runner with versioned inputs, artifacts, metrics, and reports, and make at least one verification workflow accessible through the product interface.
• Run controlled experiments across base, fine-tuned, merged, quantized, and known distilled models, improving understanding of when verification methods succeed, fail, and why.

Requirements

• Strong Python engineering skills, with hands-on experience using PyTorch and Hugging Face Transformers.
• Solid understanding of machine learning evaluation, including dataset design, baselines, metrics, calibration, false positives and negatives, statistical uncertainty, and reproducibility.
• Ability to read ML research papers critically and implement methods from first principles rather than relying entirely on existing packages.
• Professional software engineering experience beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, deployment, and documentation.
• Familiarity with open-weight models and a practical understanding of how modern LLM inference systems operate.
• Ability to work across backend and frontend boundaries, with sufficient React/TypeScript knowledge to help make complex experiments and results understandable to users.
• Strong experimental and analytical judgment, particularly around distinguishing what evidence demonstrates from what it merely suggests.
• High ownership and initiative, with the ability to identify problems, propose solutions, and drive projects forward independently.
• Comfort working in a fast-moving startup environment where priorities can change quickly and engineers may operate across multiple functions.
• Experience with model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluation, interpretability, or related areas is a plus.
• Experience with activation and representation analysis, probing, model hooks, logits, hidden states, or other model-internals techniques is advantageous.
• Familiarity with evaluation and inference infrastructure such as DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL/pgvector, or comparable technologies is beneficial.
• Experience with Next.js, React, TypeScript, data visualization, or experiment dashboards is a plus.
• Experience running and serving open-weight models on GPUs, including reasoning about latency, throughput, memory, precision, and cost trade-offs, is valuable.
• Experience designing adversarial evaluations or testing systems against deliberate attempts to evade detection is an advantage.
• A strong commitment to producing production-quality code, tests, tooling, and documentation that other engineers can confidently operate and extend.

Benefits

• Opportunity to work on applied machine learning at the intersection of research, experimentation, engineering, and product.
• End-to-end ownership across model evaluation, model internals, infrastructure, backend systems, and user-facing experiences.
• Exposure to modern open-weight models, LLM inference systems, and emerging ML verification techniques.
• A role with significant technical autonomy and the opportunity to shape both experiments and production systems.
• Fast-moving startup environment with evolving priorities and cross-functional collaboration.
• Opportunity to translate cutting-edge research into practical, measurable, and user-accessible products.
• The opportunity to build systems and evaluation methodologies designed to produce evidence that users can understand and trust.
• Location: Romania.
• Additional compensation, flexibility, healthcare, and other benefits may be provided according to the partner company's employment package and local arrangements.